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18 pages, 16650 KiB

Open AccessArticle

A Hybrid Tracking System of Full-Body Motion Inside Crowds

by Maik Boltes, Juliane Adrian and Anna-Katharina Raytarowski

Sensors 2021, 21(6), 2108; https://doi.org/10.3390/s21062108 - 17 Mar 2021

Cited by 12 | Viewed by 3450

Abstract

For our understanding of the dynamics inside crowds, reliable empirical data are needed, which could enable increases in safety and comfort for pedestrians and the design of models reflecting the real dynamics. A well-calibrated camera system can extract absolute head position with high [...] Read more.

For our understanding of the dynamics inside crowds, reliable empirical data are needed, which could enable increases in safety and comfort for pedestrians and the design of models reflecting the real dynamics. A well-calibrated camera system can extract absolute head position with high accuracy. The inclusion of inertial sensors or even self-contained full-body motion capturing systems allows the relative tracking of invisible people or body parts or capturing the locomotion of the whole body even in dense crowds. The newly introduced hybrid system maps the trajectory of the top of the head coming from a full-body motion tracking system to the head trajectory of a camera system in global space. The fused data enable the analysis of possible correlations of all observables. In this paper we present an experiment of people passing though a bottleneck and show by example the influences of bottleneck width and motivation on the overall movement, velocity, stepping locomotion and rotation of the pelvis. The hybrid tracking system opens up new possibilities for analyzing pedestrian dynamics inside crowds, such as the space requirement while passing through a bottleneck. The system allows linking any body motion to characteristics describing the situation of a person inside a crowd, such as the density or movements of other participants nearby. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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31 pages, 5717 KiB

Open AccessArticle

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

by Antonio R. Jiménez and Fernando Seco

Sensors 2021, 21(5), 1787; https://doi.org/10.3390/s21051787 - 4 Mar 2021

Cited by 28 | Viewed by 6091

Abstract

The location of people, robots, and Internet-of-Things (IoT) devices has become increasingly important. Among the available location technologies, solutions based on ultrawideband (UWB) radio are having much success due to their accuracy, which is ideally at a centimeter level. However, this accuracy is [...] Read more.

The location of people, robots, and Internet-of-Things (IoT) devices has become increasingly important. Among the available location technologies, solutions based on ultrawideband (UWB) radio are having much success due to their accuracy, which is ideally at a centimeter level. However, this accuracy is degraded in most common indoor environments due to the presence of obstacles which block or reflect the radio signals used for ranging. One way to circumvent this difficulty is through robust estimation algorithms based on measurement redundancy, permitting to minimize the effect of significantly erroneous ranges (outliers). This need for redundancy often conflicts with hardware restraints put up by the location system’s designers. In this work, we present a procedure to increase the redundancy of UWB systems and demonstrate it with the help of a commercial system made by Decawave. This system is particularly easy to deploy, by configuring a network of beacons (anchors) and devices (tags) to be located; however, its architecture presents a major disadvantage as each tag to be located can only measure ranges to a maximum of four anchors. This limitation is embedded in the Positioning and Networking Stack (PANS) protocol designed by Decawave, and therefore is not easy to bypass without a total redesign of the firmware. In this paper, we analyze the strategies that we have been able to identify in order to provide this equipment with multiple range measurements, and thus enable each tag to be positioned with more than four measured ranges. We will see the advantages and disadvantages of each of these strategies, and finally we will adopt a solution that we implemented to be able to measure up to eight ranges for each mobile device (tag). This solution implies the duplication of the tags at the mobile user, and the creation of a double interleaved network of anchors. The range among tags and the eight beacons is obtained through an API via a wireless BLE protocol at a 10 Hz rate. A robustified Extended Kalman filter (EKF) is designed to estimate, by trilateration, the position of the pair of mobile tags, using eight ranges. Two different scenarios are used to make localization experimentation: a laboratory and an apartment. Our position estimation, which exploits redundant information and performs outlier removal, is compared with the commercial solution limited to four ranges, demonstrating the need and advantages of our multi-range approach. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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22 pages, 17119 KiB

Open AccessArticle

Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

by Teodoro Aguilera, Fernando J. Aranda, Felipe Parralejo, Juan D. Gutiérrez, José A. Moreno and Fernando J. Álvarez

Sensors 2021, 21(5), 1703; https://doi.org/10.3390/s21051703 - 2 Mar 2021

Cited by 2 | Viewed by 3086

Abstract

Proximity-Based Indoor Positioning Systems (PIPSs) are a simple to install alternative in large facilities. Besides, these systems have a reduced computational cost on the mobile device of those users who do not continuously demand a high location accuracy. This work presents the design [...] Read more.

Proximity-Based Indoor Positioning Systems (PIPSs) are a simple to install alternative in large facilities. Besides, these systems have a reduced computational cost on the mobile device of those users who do not continuously demand a high location accuracy. This work presents the design of an Acoustic Low Energy (ALE) beacon based on the emission of inaudible Linear Frequency Modulated (LFM) signals. This coding scheme provides high robustness to in-band noise, thus ensuring a reliable detection of the beacon at a practical range, after pulse compression. A series of experimental tests have been carried out with nine different Android devices to study the system performance. These tests have shown that the ALE beacon can be detected at one meter distance with signal-to-noise ratios as low as −12 dB. The tests have also demonstrated a detection rate above 80% for reception angles up to 50° with respect to the beacon’s acoustic axis at the same distance. Finally, a study of the ALE beacon energy consumption has been conducted demonstrating comparable power consumption to commercial Bluetooth Low Energy (BLE) beacons. Besides, the ALE beacon search can save up to 9% more battery of the Android devices than the BLE beacon scanning. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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24 pages, 1986 KiB

Open AccessArticle

Sensing Framework for the Internet of Actors in the Value Co-Creation Process with a Beacon-Attachable Indoor Positioning System

by Keiichi Zempo, Taiga Arai, Takuya Aoki and Yukihiko Okada

Sensors 2021, 21(1), 83; https://doi.org/10.3390/s21010083 - 25 Dec 2020

Cited by 4 | Viewed by 3501

Abstract

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In [...] Read more.

To evaluate and improve the value of a service, it is important to measure not only the outcomes, but also the process of the service. Value co-creation (VCC) is not limited to outcomes, especially in interpersonal services based on interactions between actors. In this paper, a sensing framework for a VCC process in retail stores is proposed by improving an environment recognition based indoor positioning system with high positioning performance in a metal shelf environment. The conventional indoor positioning systems use radio waves; therefore, errors are caused by reflection, absorption, and interference from metal shelves. An improvement in positioning performance was achieved in the proposed method by using an IR (infrared) slit and IR light, which avoids such errors. The system was designed to recognize many and unspecified people based on the environment recognition method that the receivers had installed, in the service environment. In addition, sensor networking was also conducted by adding a function to transmit payload and identification simultaneously to the beacons that were attached to positioning objects. The effectiveness of the proposed method was verified by installing it not only in an experimental environment with ideal conditions, but posteriorly, the system was tested in real conditions, in a retail store. In our experimental setup, in a comparison with equal element numbers, positioning identification was possible within an error of 96.2 mm in a static environment in contrast to the radio wave based method where an average positioning error of approximately 648 mm was measured using the radio wave based method (Bluetooth low-energy fingerprinting technique). Moreover, when multiple beacons were used simultaneously in our system within the measurement range of one receiver, the appropriate setting of the pulse interval and jitter rate was implemented by simulation. Additionally, it was confirmed that, in a real scenario, it is possible to measure the changes in movement and positional relationships between people. This result shows the feasibility of measuring and evaluating the VCC process in retail stores, although it was difficult to measure the interaction between actors. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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22 pages, 5249 KiB

Open AccessArticle

Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation

by Vincenzo Di Pietra, Paolo Dabove and Marco Piras

Sensors 2020, 20(21), 6292; https://doi.org/10.3390/s20216292 - 5 Nov 2020

Cited by 33 | Viewed by 3762

Abstract

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors [...] Read more.

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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16 pages, 1392 KiB

Open AccessArticle

Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data

by Andy W. R. Soundy, Bradley J. Panckhurst, Phillip Brown, Andrew Martin, Timothy C. A. Molteno and Daniel Schumayer

Sensors 2020, 20(21), 6050; https://doi.org/10.3390/s20216050 - 24 Oct 2020

Cited by 7 | Viewed by 2214

Abstract

We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does [...] Read more.

We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does not account for the observed autocorrelations and has an artificially large variance. Noise-model analysis—using Akaike’s Information Criterion—favours alternative models, such as an Ornstein–Uhlenbeck or an autoregressive process. We suggest that incorporating a suitable enhanced noise model into applications (e.g., Kalman Filters) that rely on GPS position estimates will improve performance. This provides an alternative to explicitly modelling possible sources of correlation (e.g., multipath, shadowing, or other second-order physical phenomena). Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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14 pages, 1971 KiB

Open AccessArticle

Improving Real-Time Position Estimation Using Correlated Noise Models

by Andrew Martin, Matthew Parry, Andy W. R. Soundy, Bradley J. Panckhurst, Phillip Brown, Timothy C. A. Molteno and Daniel Schumayer

Sensors 2020, 20(20), 5913; https://doi.org/10.3390/s20205913 - 20 Oct 2020

Cited by 5 | Viewed by 2853

Abstract

We provide algorithms for inferring GPS (Global Positioning System) location and for quantifying the uncertainty of this estimate in real time. The algorithms are tested on GPS data from locations in the Southern Hemisphere at four significantly different latitudes. In order to rank [...] Read more.

We provide algorithms for inferring GPS (Global Positioning System) location and for quantifying the uncertainty of this estimate in real time. The algorithms are tested on GPS data from locations in the Southern Hemisphere at four significantly different latitudes. In order to rank the algorithms, we use the so-called log-score rule. The best algorithm uses an Ornstein–Uhlenbeck (OU) noise model and is built on an enhanced Kalman Filter (KF). The noise model is capable of capturing the observed autocorrelated process noise in the altitude, latitude and longitude recordings. This model outperforms a KF that assumes a Gaussian noise model, which under-reports the position uncertainties. We also found that the dilution-of-precision parameters, automatically reported by the GPS receiver at no additional cost, do not help significantly in the uncertainty quantification of the GPS positioning. A non-learning method using the actual position measurements and employing a constant uncertainty does not even converge to the correct position. Inference with the enhanced noise model is suitable for embedded computing and capable of achieving real-time position inference, can quantify uncertainty and be extended to incorporate complementary sensor recordings, e.g., from an accelerometer or from a magnetometer, in order to improve accuracy. The algorithm corresponding to the augmented-state unscented KF method suggests a computational cost of

O (d_{x}^{2} d_{t})

, where

d_{x}

is the dimension of the augmented state-vector and

d_{t}

is an adjustable, design-dependent parameter corresponding to the length of “past values” one wishes to keep for re-evaluation of the model from time to time. The provided algorithm assumes

d_{t} = 1

. Hence, the algorithm is likely to be suitable for sensor fusion applications. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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31 pages, 1451 KiB

Open AccessArticle

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

by Miroslav Opiela and František Galčík

Sensors 2020, 20(18), 5343; https://doi.org/10.3390/s20185343 - 18 Sep 2020

Cited by 7 | Viewed by 3054

Abstract

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which [...] Read more.

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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19 pages, 486 KiB

Open AccessArticle

Analysis of Received Signal Strength Quantization in Fingerprinting Localization

by Syed Khandker, Joaquín Torres-Sospedra and Tapani Ristaniemi

Sensors 2020, 20(11), 3203; https://doi.org/10.3390/s20113203 - 4 Jun 2020

Cited by 7 | Viewed by 3747

Abstract

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. [...] Read more.

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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17 pages, 1736 KiB

Open AccessArticle

Automatic Calibration of the Step Length Model of a Pocket INS by Means of a Foot Inertial Sensor

by Dina Bousdar Ahmed, Estefania Munoz Diaz and Juan Jesús García Domínguez

Sensors 2020, 20(7), 2083; https://doi.org/10.3390/s20072083 - 7 Apr 2020

Cited by 11 | Viewed by 3010

Abstract

All non-foot-mounted inertial localization systems have a common challenge: the need for calibrating the parameters of the step length model. The calibration of the parameters of a step length model is key for an accurate estimation of the pedestrian’s step length, and therefore, [...] Read more.

All non-foot-mounted inertial localization systems have a common challenge: the need for calibrating the parameters of the step length model. The calibration of the parameters of a step length model is key for an accurate estimation of the pedestrian’s step length, and therefore, for the accuracy of the position estimation. In a previous work, we provided a proof of concept on how to calibrate step length models with a foot inertial navigation system (INS), i.e., an INS based on an inertial measurement unit (IMU) mounted on the upper front part of the foot. The reason is that the foot INS does not require calibration thanks to the implementation of the strapdown algorithm. The goal of this article is to automatically calibrate the parameters of a step length model of the pocket INS by means of the foot INS. The step length model of the pocket INS has two parameters: the slope and offset of a first-order linear regression that relates the amplitude of the thigh pitch with the user’s step length. Firstly, we show that it is necessary to estimate the two parameters of the step length model. Secondly, we propose a method to automatically estimate these parameters by means of a foot INS. Finally, we propose a practical implementation of the proposed method in the pocket INS. We evaluate the pocket INS with the proposed calibration method and we compare the results to the state of the art implementations of the pocket INS. The results show that the proposed automatic calibration method outperforms the previous work, which proves the need for calibrating all the parameters of the step length model of the pocket INS. In this work, we conclude that it is possible to use a foot INS to automatically calibrate all parameters of the step length model of the pocket INS. Since the calibration of the step length model is always needed, our proposed automatic calibration method is a key enabler for using the pocket INS. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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39 pages, 4936 KiB

Open AccessEditor’s ChoiceSystematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

by Pavel Pascacio, Sven Casteleyn, Joaquín Torres-Sospedra, Elena Simona Lohan and Jari Nurmi

Sensors 2021, 21(3), 1002; https://doi.org/10.3390/s21031002 - 2 Feb 2021

Cited by 96 | Viewed by 11956

Abstract

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning [...] Read more.

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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14 pages, 1681 KiB

Open AccessLetter

Estimating Position from Millisecond Samples of GPS Signals (the “FastFix” Algorithm)

by Timothy C. A. Molteno

Sensors 2020, 20(22), 6480; https://doi.org/10.3390/s20226480 - 13 Nov 2020

Cited by 5 | Viewed by 2523

Abstract

A new approach to GPS positioning is described in which the post-processing of ultra-short sequences of captured GPS signal data can produce an estimate of receiver location. The algorithm, called ‘FastFix’, needs only 2–4 ms of stored L1-band data sampled at ∼16 MHz. [...] Read more.

A new approach to GPS positioning is described in which the post-processing of ultra-short sequences of captured GPS signal data can produce an estimate of receiver location. The algorithm, called ‘FastFix’, needs only 2–4 ms of stored L1-band data sampled at ∼16 MHz. The algorithm uses a least-squares optimization to estimate receiver position and GPS time from measurements of the relative codephase, and Doppler-shift of GNSS satellite signals. A practical application of this algorithm is demonstrated in a small, lightweight, low-power tracking tag that periodically wakes-up, records and stores 4 ms of GPS L1-band signal and returns to a low-power state—reducing power requirements by a factor of ∼10,000 compared to typical GPS devices. Stationary device testing shows a median error of 27.7 m with a small patch antenna. Results from deployment of this tag on adult Royal Albatross show excellent performance, demonstrating lightweight, solar-powered, long-term tracking of these remarkable birds. This work was performed on the GPS system; however, the algorithm is applicable to other GNSS systems. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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